d1e95af282
Signed-off-by: Xinyuan Tong <xinyuantong.cs@gmail.com> Co-authored-by: Kangyan-Zhou <zky314343421@gmail.com> Co-authored-by: Alison Shao <alisonshao@mac.lan> Co-authored-by: Mick <mickjagger19@icloud.com>
1442 lines
53 KiB
Python
1442 lines
53 KiB
Python
# Copyright 2025 Qwen Team
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# Copyright 2025 SGLang Team
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Inference-only Qwen3.5 model and Qwen3.5 MoE model compatible with HuggingFace weights."""
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import logging
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from functools import lru_cache
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from typing import Iterable, Optional, Set, Tuple, Union
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import torch
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import torch.nn as nn
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# Configs
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from sglang.srt.configs.qwen3_5 import (
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Qwen3_5Config,
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Qwen3_5MoeConfig,
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Qwen3_5TextConfig,
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)
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# Distributed
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from sglang.srt.distributed import get_pp_group, get_pp_indices
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from sglang.srt.eplb.expert_distribution import get_global_expert_distribution_recorder
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from sglang.srt.eplb.expert_location import ModelConfigForExpertLocation
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# Layers - Attention
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from sglang.srt.layers.attention.fla.layernorm_gated import RMSNorm as RMSNormGated
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from sglang.srt.layers.attention.mamba.mamba import mamba_v2_sharded_weight_loader
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from sglang.srt.layers.communicator import LayerCommunicator, LayerScatterModes
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from sglang.srt.layers.dp_attention import (
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get_attention_tp_rank,
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get_attention_tp_size,
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is_dp_attention_enabled,
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)
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# Layers - Others
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from sglang.srt.layers.layernorm import GemmaRMSNorm
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# Layers - Linear
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from sglang.srt.layers.linear import (
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ColumnParallelLinear,
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MergedColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear,
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)
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from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE
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from sglang.srt.layers.quantization.base_config import QuantizationConfig
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from sglang.srt.layers.radix_attention import RadixAttention
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from sglang.srt.layers.radix_linear_attention import RadixLinearAttention
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from sglang.srt.layers.rotary_embedding import get_rope
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from sglang.srt.layers.utils import PPMissingLayer
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from sglang.srt.layers.vocab_parallel_embedding import VocabParallelEmbedding
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from sglang.srt.model_executor.cuda_graph_runner import get_is_capture_mode
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from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
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from sglang.srt.model_loader.weight_utils import (
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default_weight_loader,
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sharded_weight_loader,
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)
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from sglang.srt.models.qwen2_moe import Qwen2MoeMLP, Qwen2MoeSparseMoeBlock
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# Models
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from sglang.srt.models.qwen3_vl import Qwen3VLForConditionalGeneration
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# Utils
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from sglang.srt.utils import (
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LazyValue,
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add_prefix,
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is_cuda,
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is_npu,
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make_layers,
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set_weight_attrs,
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)
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from sglang.srt.utils.hf_transformers_utils import get_processor, get_rope_config
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logger = logging.getLogger(__name__)
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_is_cuda = is_cuda()
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_is_npu = is_npu()
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cached_get_processor = lru_cache(get_processor)
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class Qwen3_5GatedDeltaNet(nn.Module):
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def __init__(
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self,
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config: Qwen3_5TextConfig,
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layer_id: int,
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quant_config: Optional[QuantizationConfig] = None,
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alt_stream: Optional[torch.cuda.Stream] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.config = config
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self.attn_tp_rank = get_attention_tp_rank()
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self.attn_tp_size = get_attention_tp_size()
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self.hidden_size = config.hidden_size
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self.num_v_heads = config.linear_num_value_heads
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self.num_k_heads = config.linear_num_key_heads
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self.head_k_dim = config.linear_key_head_dim
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self.head_v_dim = config.linear_value_head_dim
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self.key_dim = self.head_k_dim * self.num_k_heads
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self.value_dim = self.head_v_dim * self.num_v_heads
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self.alt_stream = alt_stream
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self.conv_kernel_size = config.linear_conv_kernel_dim
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self.layer_id = layer_id
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self.activation = config.hidden_act
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self.layer_norm_epsilon = config.rms_norm_eps
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# Conv1d layer
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self.conv_dim = self.key_dim * 2 + self.value_dim
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self.conv1d = ColumnParallelLinear(
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input_size=self.conv_kernel_size,
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output_size=self.conv_dim,
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bias=False,
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quant_config=None,
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tp_rank=self.attn_tp_rank,
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tp_size=self.attn_tp_size,
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prefix=add_prefix("conv1d", prefix),
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)
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self.conv1d.weight.data = self.conv1d.weight.data.unsqueeze(1)
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# Split projection layers (following vLLM's implementation)
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# Instead of fused in_proj_qkvz and in_proj_ba, use separate layers
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self.in_proj_qkv = MergedColumnParallelLinear(
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input_size=self.hidden_size,
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output_sizes=[self.key_dim, self.key_dim, self.value_dim],
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bias=False,
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quant_config=quant_config,
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tp_rank=self.attn_tp_rank,
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tp_size=self.attn_tp_size,
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prefix=add_prefix("in_proj_qkv", prefix),
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)
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self.in_proj_z = ColumnParallelLinear(
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input_size=self.hidden_size,
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output_size=self.value_dim,
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bias=False,
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quant_config=quant_config,
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tp_rank=self.attn_tp_rank,
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tp_size=self.attn_tp_size,
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prefix=add_prefix("in_proj_z", prefix),
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)
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self.in_proj_b = ColumnParallelLinear(
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input_size=self.hidden_size,
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output_size=self.num_v_heads,
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bias=False,
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quant_config=quant_config,
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tp_rank=self.attn_tp_rank,
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tp_size=self.attn_tp_size,
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prefix=add_prefix("in_proj_b", prefix),
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)
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self.in_proj_a = ColumnParallelLinear(
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input_size=self.hidden_size,
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output_size=self.num_v_heads,
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bias=False,
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quant_config=quant_config,
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tp_rank=self.attn_tp_rank,
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tp_size=self.attn_tp_size,
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prefix=add_prefix("in_proj_a", prefix),
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)
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# Conv1d weight loader setup
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query_key_settings = (self.key_dim, 0, False)
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value_settings = (self.value_dim, 0, False)
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delattr(self.conv1d.weight, "weight_loader")
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set_weight_attrs(
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self.conv1d.weight,
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{
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"weight_loader": mamba_v2_sharded_weight_loader(
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[
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query_key_settings,
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query_key_settings,
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value_settings,
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],
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self.attn_tp_size,
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self.attn_tp_rank,
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)
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},
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)
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# State parameters
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self.dt_bias = nn.Parameter(
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torch.ones(self.num_v_heads // self.attn_tp_size),
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)
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self.A_log = nn.Parameter(
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torch.empty(self.num_v_heads // self.attn_tp_size, dtype=torch.float32),
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)
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set_weight_attrs(self.A_log, {"weight_loader": sharded_weight_loader(0)})
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set_weight_attrs(self.dt_bias, {"weight_loader": sharded_weight_loader(0)})
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conv_weights = self.conv1d.weight.view(
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self.conv1d.weight.size(0), self.conv1d.weight.size(2)
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)
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# RadixLinearAttention layer
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self.attn = RadixLinearAttention(
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layer_id=layer_id,
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num_q_heads=self.num_k_heads // self.attn_tp_size,
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num_k_heads=self.num_k_heads // self.attn_tp_size,
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num_v_heads=self.num_v_heads // self.attn_tp_size,
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head_q_dim=self.head_k_dim,
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head_k_dim=self.head_k_dim,
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head_v_dim=self.head_v_dim,
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conv_weights=conv_weights,
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bias=self.conv1d.bias,
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activation=self.activation,
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A_log=self.A_log,
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dt_bias=self.dt_bias,
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)
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# Normalization layer
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self.norm = RMSNormGated(
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self.head_v_dim,
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eps=self.layer_norm_epsilon,
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group_size=None,
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norm_before_gate=True,
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device=torch.get_device_module().current_device(),
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dtype=config.torch_dtype,
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)
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# Output projection
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self.out_proj = RowParallelLinear(
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self.value_dim,
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self.hidden_size,
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bias=False,
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input_is_parallel=True,
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reduce_results=False,
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quant_config=quant_config,
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tp_rank=self.attn_tp_rank,
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tp_size=self.attn_tp_size,
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prefix=add_prefix("out_proj", prefix),
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)
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def fix_query_key_value_ordering(
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self,
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mixed_qkv,
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z,
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b,
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a,
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):
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raise NotImplementedError(
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"Qwen3.5 Series dont need to fix query key value ordering"
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)
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def forward(
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self,
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hidden_states: torch.Tensor,
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forward_batch: ForwardBatch,
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):
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"""
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Forward pass with three parts:
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1. Input projection
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2. Core attention (custom op)
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3. Output projection
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"""
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seq_len, _ = hidden_states.shape
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mixed_qkv, _ = self.in_proj_qkv(hidden_states)
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z, _ = self.in_proj_z(hidden_states)
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z = z.reshape(z.size(0), -1, self.head_v_dim)
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b, _ = self.in_proj_b(hidden_states)
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a, _ = self.in_proj_a(hidden_states)
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b = b.contiguous()
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a = a.contiguous()
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core_attn_out = self.attn(
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forward_batch=forward_batch,
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mixed_qkv=mixed_qkv,
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a=a,
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b=b,
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)
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z_shape_og = z.shape
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core_attn_out = core_attn_out.reshape(-1, core_attn_out.shape[-1])
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z = z.reshape(-1, z.shape[-1])
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core_attn_out = self.norm(core_attn_out, z)
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core_attn_out = core_attn_out.reshape(z_shape_og)
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core_attn_out = core_attn_out.flatten(-2) # ... h d -> ... (h d)
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output, _ = self.out_proj(core_attn_out)
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return output
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class Qwen3_5LinearDecoderLayer(nn.Module):
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"""Qwen3.5 Decoder Layer with Linear Attention (GatedDeltaNet)."""
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def __init__(
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self,
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config: Qwen3_5TextConfig,
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layer_id: int,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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alt_stream: Optional[torch.cuda.Stream] = None,
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is_nextn: bool = False,
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) -> None:
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super().__init__()
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self.config = config
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self.layer_id = layer_id
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linear_attn_quant_config = (
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None
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if quant_config and quant_config.get_name() == "modelopt_fp4"
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else quant_config
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)
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self.linear_attn = Qwen3_5GatedDeltaNet(
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config, layer_id, linear_attn_quant_config, alt_stream, prefix
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)
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# NOTE: Determine the MLP type based on the model type
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# Qwen3.5 use all layers for MLP / Qwen3.5-MoE use sparse MoE blocks
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if config.model_type == "qwen3_5_moe_text":
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self.mlp = Qwen2MoeSparseMoeBlock(
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layer_id=layer_id,
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config=config,
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quant_config=quant_config,
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alt_stream=alt_stream,
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prefix=add_prefix("mlp", prefix.replace(".linear_attn", "")),
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is_nextn=is_nextn,
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)
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is_layer_sparse = True
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is_previous_layer_sparse = True
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is_next_layer_sparse = True
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elif config.model_type == "qwen3_5_text":
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self.mlp = Qwen2MoeMLP(
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hidden_size=config.hidden_size,
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intermediate_size=config.intermediate_size,
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hidden_act=config.hidden_act,
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quant_config=quant_config,
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prefix=add_prefix("mlp", prefix.replace(".linear_attn", "")),
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)
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is_layer_sparse = False
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is_previous_layer_sparse = False
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is_next_layer_sparse = False
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else:
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raise ValueError(f"Invalid model type: {config.model_type}")
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self.layer_scatter_modes = LayerScatterModes.init_new(
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layer_id=layer_id,
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num_layers=config.num_hidden_layers,
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is_layer_sparse=is_layer_sparse,
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is_previous_layer_sparse=is_previous_layer_sparse,
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is_next_layer_sparse=is_next_layer_sparse,
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)
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self.input_layernorm = GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_attention_layernorm = GemmaRMSNorm(
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config.hidden_size, eps=config.rms_norm_eps
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)
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self.layer_communicator = LayerCommunicator(
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layer_scatter_modes=self.layer_scatter_modes,
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input_layernorm=self.input_layernorm,
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post_attention_layernorm=self.post_attention_layernorm,
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allow_reduce_scatter=True,
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is_last_layer=(layer_id == config.num_hidden_layers - 1),
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)
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def forward(
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self,
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hidden_states: torch.Tensor,
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residual: Optional[torch.Tensor],
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**kwargs,
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):
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forward_batch = kwargs.get("forward_batch", None)
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hidden_states, residual = self.layer_communicator.prepare_attn(
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hidden_states, residual, forward_batch
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)
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if not forward_batch.forward_mode.is_idle():
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hidden_states = self.linear_attn(
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hidden_states,
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forward_batch,
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)
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# Fully Connected
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hidden_states, residual = self.layer_communicator.prepare_mlp(
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hidden_states, residual, forward_batch
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)
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use_reduce_scatter = self.layer_communicator.should_use_reduce_scatter(
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forward_batch
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)
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should_allreduce_fusion = (
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self.layer_communicator.should_fuse_mlp_allreduce_with_next_layer(
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forward_batch
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)
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)
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if isinstance(self.mlp, Qwen2MoeSparseMoeBlock):
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hidden_states = self.mlp(
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hidden_states,
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forward_batch,
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use_reduce_scatter,
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should_allreduce_fusion,
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)
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else:
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hidden_states = self.mlp(
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hidden_states, should_allreduce_fusion, use_reduce_scatter
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)
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if should_allreduce_fusion:
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hidden_states._sglang_needs_allreduce_fusion = True
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else:
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hidden_states, residual = self.layer_communicator.postprocess_layer(
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hidden_states, residual, forward_batch
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)
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return hidden_states, residual
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|
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class Qwen3_5AttentionDecoderLayer(nn.Module):
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"""Qwen3.5 Decoder Layer with Full Attention."""
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def __init__(
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self,
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config: Qwen3_5TextConfig,
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layer_id: int,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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alt_stream: Optional[torch.cuda.Stream] = None,
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is_nextn: bool = False,
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) -> None:
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super().__init__()
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self.config = config
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self.hidden_size = config.hidden_size
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self.attn_tp_rank = get_attention_tp_rank()
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self.attn_tp_size = get_attention_tp_size()
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self.total_num_heads = config.num_attention_heads
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assert self.total_num_heads % self.attn_tp_size == 0
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self.num_heads = self.total_num_heads // self.attn_tp_size
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self.total_num_kv_heads = config.num_key_value_heads
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if self.total_num_kv_heads >= self.attn_tp_size:
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assert self.total_num_kv_heads % self.attn_tp_size == 0
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else:
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assert self.attn_tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // self.attn_tp_size)
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self.head_dim = config.head_dim or (self.hidden_size // self.num_heads)
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.scaling = self.head_dim**-0.5
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self.max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
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self.rope_theta, rope_scaling = get_rope_config(config)
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self.partial_rotary_factor = getattr(config, "partial_rotary_factor", 1.0)
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self.layer_id = layer_id
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# If rope_scaling doesn't specify a scaling type, treat as no scaling
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if rope_scaling and not ("rope_type" in rope_scaling or "type" in rope_scaling):
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rope_scaling = None
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self.attn_output_gate = getattr(config, "attn_output_gate", True)
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if self.attn_output_gate:
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logger.warning_once("using attn output gate!")
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|
|
self.rotary_emb = get_rope(
|
|
head_size=self.head_dim,
|
|
rotary_dim=self.head_dim,
|
|
max_position=self.max_position_embeddings,
|
|
rope_scaling=rope_scaling,
|
|
base=self.rope_theta,
|
|
partial_rotary_factor=self.partial_rotary_factor,
|
|
is_neox_style=True,
|
|
dtype=torch.get_default_dtype(),
|
|
)
|
|
|
|
attn_quant_config = (
|
|
None
|
|
if quant_config and quant_config.get_name() == "modelopt_fp4"
|
|
else quant_config
|
|
)
|
|
|
|
self.qkv_proj = QKVParallelLinear(
|
|
config.hidden_size,
|
|
self.head_dim,
|
|
self.total_num_heads * (1 + self.attn_output_gate),
|
|
self.total_num_kv_heads,
|
|
bias=False,
|
|
quant_config=attn_quant_config,
|
|
tp_rank=self.attn_tp_rank,
|
|
tp_size=self.attn_tp_size,
|
|
prefix=add_prefix("qkv_proj", prefix),
|
|
)
|
|
|
|
self.o_proj = RowParallelLinear(
|
|
self.total_num_heads * self.head_dim,
|
|
config.hidden_size,
|
|
bias=False,
|
|
quant_config=attn_quant_config,
|
|
reduce_results=False,
|
|
tp_rank=self.attn_tp_rank,
|
|
tp_size=self.attn_tp_size,
|
|
prefix=add_prefix("o_proj", prefix),
|
|
)
|
|
|
|
self.attn = RadixAttention(
|
|
self.num_heads,
|
|
self.head_dim,
|
|
self.scaling,
|
|
num_kv_heads=self.num_kv_heads,
|
|
layer_id=layer_id,
|
|
prefix=f"{prefix}.attn",
|
|
)
|
|
|
|
# Dense MLP for non-MoE variant
|
|
if config.model_type == "qwen3_5_text":
|
|
self.mlp = Qwen2MoeMLP(
|
|
hidden_size=config.hidden_size,
|
|
intermediate_size=config.intermediate_size,
|
|
hidden_act=config.hidden_act,
|
|
quant_config=quant_config,
|
|
prefix=add_prefix("mlp", prefix.replace(".self_attn", "")),
|
|
)
|
|
is_layer_sparse = False
|
|
is_previous_layer_sparse = False
|
|
is_next_layer_sparse = False
|
|
elif config.model_type == "qwen3_5_moe_text":
|
|
self.mlp = Qwen2MoeSparseMoeBlock(
|
|
layer_id=layer_id,
|
|
config=config,
|
|
quant_config=quant_config,
|
|
alt_stream=alt_stream,
|
|
prefix=add_prefix("mlp", prefix.replace(".self_attn", "")),
|
|
is_nextn=is_nextn,
|
|
)
|
|
is_layer_sparse = True
|
|
is_previous_layer_sparse = True
|
|
is_next_layer_sparse = True
|
|
else:
|
|
raise ValueError(f"Invalid model type: {config.model_type}")
|
|
|
|
self.layer_scatter_modes = LayerScatterModes.init_new(
|
|
layer_id=layer_id,
|
|
num_layers=config.num_hidden_layers,
|
|
is_layer_sparse=is_layer_sparse,
|
|
is_previous_layer_sparse=is_previous_layer_sparse,
|
|
is_next_layer_sparse=is_next_layer_sparse,
|
|
)
|
|
|
|
self.input_layernorm = GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.post_attention_layernorm = GemmaRMSNorm(
|
|
config.hidden_size, eps=config.rms_norm_eps
|
|
)
|
|
|
|
self.q_norm = GemmaRMSNorm(self.head_dim, eps=config.rms_norm_eps)
|
|
self.k_norm = GemmaRMSNorm(self.head_dim, eps=config.rms_norm_eps)
|
|
|
|
self.layer_communicator = LayerCommunicator(
|
|
layer_scatter_modes=self.layer_scatter_modes,
|
|
input_layernorm=self.input_layernorm,
|
|
post_attention_layernorm=self.post_attention_layernorm,
|
|
allow_reduce_scatter=True,
|
|
is_last_layer=(layer_id == config.num_hidden_layers - 1),
|
|
)
|
|
|
|
self.alt_stream = alt_stream
|
|
|
|
def _apply_qk_norm(
|
|
self, q: torch.Tensor, k: torch.Tensor
|
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
"""Apply Q/K normalization with optional alt_stream overlap."""
|
|
if self.alt_stream is not None and get_is_capture_mode():
|
|
current_stream = torch.cuda.current_stream()
|
|
self.alt_stream.wait_stream(current_stream)
|
|
q_by_head = q.reshape(-1, self.head_dim)
|
|
q_by_head = self.q_norm(q_by_head)
|
|
with torch.cuda.stream(self.alt_stream):
|
|
k_by_head = k.reshape(-1, self.head_dim)
|
|
k_by_head = self.k_norm(k_by_head)
|
|
current_stream.wait_stream(self.alt_stream)
|
|
else:
|
|
q_by_head = q.reshape(-1, self.head_dim)
|
|
q_by_head = self.q_norm(q_by_head)
|
|
k_by_head = k.reshape(-1, self.head_dim)
|
|
k_by_head = self.k_norm(k_by_head)
|
|
q = q_by_head.view(q.shape)
|
|
k = k_by_head.view(k.shape)
|
|
return q, k
|
|
|
|
def self_attention(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
) -> torch.Tensor:
|
|
"""Full attention forward pass."""
|
|
qkv, _ = self.qkv_proj(hidden_states)
|
|
|
|
if self.attn_output_gate:
|
|
q_gate, k, v = qkv.split(
|
|
[self.q_size * 2, self.kv_size, self.kv_size], dim=-1
|
|
)
|
|
orig_shape = q_gate.shape[:-1]
|
|
q_gate = q_gate.view(*orig_shape, self.num_heads, -1)
|
|
q, gate = torch.chunk(q_gate, 2, dim=-1)
|
|
q = q.reshape(*orig_shape, -1)
|
|
gate = gate.reshape(*orig_shape, -1)
|
|
else:
|
|
q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
|
|
|
|
q, k = self._apply_qk_norm(q, k)
|
|
q, k = self.rotary_emb(positions, q, k)
|
|
attn_output = self.attn(q, k, v, forward_batch)
|
|
|
|
if self.attn_output_gate:
|
|
gate = torch.sigmoid(gate)
|
|
attn_output = attn_output * gate
|
|
|
|
output, _ = self.o_proj(attn_output)
|
|
return output
|
|
|
|
def forward(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
residual: Optional[torch.Tensor],
|
|
forward_batch: ForwardBatch,
|
|
**kwargs,
|
|
):
|
|
hidden_states, residual = self.layer_communicator.prepare_attn(
|
|
hidden_states, residual, forward_batch
|
|
)
|
|
|
|
if not forward_batch.forward_mode.is_idle():
|
|
hidden_states = self.self_attention(
|
|
positions=positions,
|
|
hidden_states=hidden_states,
|
|
forward_batch=forward_batch,
|
|
)
|
|
|
|
# Fully Connected
|
|
hidden_states, residual = self.layer_communicator.prepare_mlp(
|
|
hidden_states, residual, forward_batch
|
|
)
|
|
use_reduce_scatter = self.layer_communicator.should_use_reduce_scatter(
|
|
forward_batch
|
|
)
|
|
|
|
should_allreduce_fusion = (
|
|
self.layer_communicator.should_fuse_mlp_allreduce_with_next_layer(
|
|
forward_batch
|
|
)
|
|
)
|
|
if isinstance(self.mlp, Qwen2MoeSparseMoeBlock):
|
|
hidden_states = self.mlp(
|
|
hidden_states,
|
|
forward_batch,
|
|
use_reduce_scatter,
|
|
should_allreduce_fusion,
|
|
)
|
|
else:
|
|
hidden_states = self.mlp(
|
|
hidden_states, should_allreduce_fusion, use_reduce_scatter
|
|
)
|
|
if should_allreduce_fusion:
|
|
hidden_states._sglang_needs_allreduce_fusion = True
|
|
else:
|
|
hidden_states, residual = self.layer_communicator.postprocess_layer(
|
|
hidden_states, residual, forward_batch
|
|
)
|
|
|
|
return hidden_states, residual
|
|
|
|
|
|
ALL_DECODER_LAYER_TYPES = {
|
|
"attention": Qwen3_5AttentionDecoderLayer,
|
|
"linear_attention": Qwen3_5LinearDecoderLayer,
|
|
}
|
|
|
|
|
|
class Qwen3_5ForCausalLM(nn.Module):
|
|
"""Qwen3.5 Model with support for dense variant."""
|
|
|
|
def __init__(
|
|
self,
|
|
config: Qwen3_5TextConfig,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "",
|
|
is_nextn: bool = False,
|
|
) -> None:
|
|
super().__init__()
|
|
self.config = config
|
|
self.hidden_size = config.hidden_size
|
|
self.pp_group = get_pp_group()
|
|
|
|
alt_stream = torch.cuda.Stream() if _is_cuda else None
|
|
|
|
# Embedding layer
|
|
if self.pp_group.is_first_rank:
|
|
self.embed_tokens = VocabParallelEmbedding(
|
|
config.vocab_size,
|
|
config.hidden_size,
|
|
org_num_embeddings=config.vocab_size,
|
|
enable_tp=not is_dp_attention_enabled(),
|
|
)
|
|
else:
|
|
self.embed_tokens = PPMissingLayer()
|
|
|
|
# Decoder layers
|
|
def get_layer(idx: int, prefix: str):
|
|
layer_type = config.layers_block_type[idx]
|
|
layer_class = ALL_DECODER_LAYER_TYPES[layer_type]
|
|
if layer_type == "attention":
|
|
prefix = add_prefix("self_attn", prefix)
|
|
else:
|
|
prefix = add_prefix("linear_attn", prefix)
|
|
return layer_class(
|
|
config=config,
|
|
layer_id=idx,
|
|
quant_config=quant_config,
|
|
prefix=prefix,
|
|
alt_stream=alt_stream,
|
|
is_nextn=is_nextn,
|
|
)
|
|
|
|
self.layers = make_layers(
|
|
config.num_hidden_layers,
|
|
get_layer,
|
|
prefix=f"{prefix}.layers",
|
|
)
|
|
|
|
pp_rank = self.pp_group.rank_in_group
|
|
pp_size = self.pp_group.world_size
|
|
num_layers = config.num_hidden_layers
|
|
self._start_layer, self._end_layer = (
|
|
get_pp_indices(
|
|
num_layers,
|
|
pp_rank,
|
|
pp_size,
|
|
)
|
|
if pp_rank is not None and pp_size is not None
|
|
else (0, num_layers)
|
|
)
|
|
|
|
# Final normalization
|
|
if self.pp_group.is_last_rank:
|
|
self.norm = GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
else:
|
|
self.norm = PPMissingLayer()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.embed_tokens
|
|
|
|
@property
|
|
def start_layer(self) -> int:
|
|
return self._start_layer
|
|
|
|
@property
|
|
def end_layer(self) -> int:
|
|
return self._end_layer
|
|
|
|
@torch.no_grad()
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
input_embeds: Optional[torch.Tensor] = None,
|
|
pp_proxy_tensors: Optional[PPProxyTensors] = None,
|
|
input_deepstack_embeds: Optional[torch.Tensor] = None,
|
|
) -> Union[torch.Tensor, PPProxyTensors]:
|
|
# Initialize hidden states
|
|
if self.pp_group.is_first_rank:
|
|
if input_embeds is None:
|
|
hidden_states = self.embed_tokens(input_ids)
|
|
else:
|
|
hidden_states = input_embeds
|
|
residual = None
|
|
else:
|
|
assert pp_proxy_tensors is not None
|
|
hidden_states = pp_proxy_tensors["hidden_states"]
|
|
residual = pp_proxy_tensors["residual"]
|
|
|
|
# Pass through decoder layers
|
|
for layer_idx in range(self.start_layer, self.end_layer):
|
|
layer = self.layers[layer_idx]
|
|
with get_global_expert_distribution_recorder().with_current_layer(
|
|
layer_idx
|
|
):
|
|
hidden_states, residual = layer(
|
|
positions=positions,
|
|
hidden_states=hidden_states,
|
|
residual=residual,
|
|
forward_batch=forward_batch,
|
|
)
|
|
|
|
# Process deepstack embeddings if provided
|
|
if (
|
|
input_deepstack_embeds is not None
|
|
and input_deepstack_embeds.numel() > 0
|
|
and layer_idx < 3
|
|
):
|
|
sep = self.hidden_size * layer_idx
|
|
hidden_states.add_(
|
|
input_deepstack_embeds[:, sep : sep + self.hidden_size]
|
|
)
|
|
|
|
# Return intermediate tensors for pipeline parallelism
|
|
if not self.pp_group.is_last_rank:
|
|
return PPProxyTensors(
|
|
{
|
|
"hidden_states": hidden_states,
|
|
"residual": residual,
|
|
}
|
|
)
|
|
|
|
# Apply final normalization
|
|
if hidden_states.shape[0] != 0:
|
|
if residual is None:
|
|
hidden_states = self.norm(hidden_states)
|
|
else:
|
|
hidden_states, _ = self.norm(hidden_states, residual)
|
|
|
|
return hidden_states
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
|
stacked_params_mapping = [
|
|
# (param_name, shard_name, shard_id)
|
|
("qkv_proj", "q_proj", "q"),
|
|
("qkv_proj", "k_proj", "k"),
|
|
("qkv_proj", "v_proj", "v"),
|
|
("gate_up_proj", "gate_proj", 0),
|
|
("gate_up_proj", "up_proj", 1),
|
|
]
|
|
|
|
loaded_params: Set[str] = set()
|
|
params_dict = dict(self.named_parameters(remove_duplicate=False))
|
|
for name, loaded_weight in weights:
|
|
if "rotary_emb.inv_freq" in name:
|
|
continue
|
|
if "mtp" in name:
|
|
continue
|
|
if "visual" in name:
|
|
continue
|
|
if "language_model" in name:
|
|
name = name.replace(r"model.language_model.", r"model.")
|
|
if ".self_attn." in name:
|
|
name = name.replace(".self_attn", "")
|
|
|
|
for param_name, weight_name, shard_id in stacked_params_mapping:
|
|
if weight_name not in name:
|
|
continue
|
|
|
|
if "mlp.experts" in name:
|
|
continue
|
|
|
|
name = name.replace(weight_name, param_name)
|
|
# Skip loading extra bias for GPTQ models.
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
# Skip layers on other devices.
|
|
# if is_pp_missing_parameter(name, self):
|
|
# continue
|
|
if name not in params_dict:
|
|
continue
|
|
param = params_dict[name]
|
|
weight_loader = getattr(param, "weight_loader")
|
|
weight_loader(param, loaded_weight, shard_id)
|
|
break
|
|
else:
|
|
# Skip loading extra bias for GPTQ models.
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
if name not in params_dict:
|
|
logger.warning(f"Parameter {name} not found in params_dict")
|
|
continue
|
|
param = params_dict[name]
|
|
|
|
weight_loader = getattr(param, "weight_loader", default_weight_loader)
|
|
weight_loader(param, loaded_weight)
|
|
loaded_params.add(name)
|
|
return loaded_params
|
|
|
|
@classmethod
|
|
def get_model_config_for_expert_location(cls, config):
|
|
return ModelConfigForExpertLocation(
|
|
num_layers=config.num_hidden_layers,
|
|
num_logical_experts=config.num_experts,
|
|
num_groups=None,
|
|
)
|
|
|
|
|
|
class Qwen3_5MoeForCausalLM(Qwen3_5ForCausalLM):
|
|
def __init__(
|
|
self,
|
|
config: Qwen3_5TextConfig,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "",
|
|
) -> None:
|
|
super().__init__(config=config, quant_config=quant_config, prefix=prefix)
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
|
stacked_params_mapping = [
|
|
# (param_name, shard_name, shard_id)
|
|
("qkv_proj", "q_proj", "q"),
|
|
("qkv_proj", "k_proj", "k"),
|
|
("qkv_proj", "v_proj", "v"),
|
|
("gate_up_proj", "gate_proj", 0),
|
|
("gate_up_proj", "up_proj", 1),
|
|
]
|
|
|
|
# Params for weights, fp8 weight scales, fp8 activation scales
|
|
# (param_name, weight_name, expert_id, shard_id)
|
|
expert_params_mapping = FusedMoE.make_expert_params_mapping(
|
|
ckpt_gate_proj_name="gate_proj",
|
|
ckpt_down_proj_name="down_proj",
|
|
ckpt_up_proj_name="up_proj",
|
|
num_experts=self.config.num_experts,
|
|
)
|
|
|
|
# Skip loading extra parameters for GPTQ/modelopt models.
|
|
ignore_suffixes = (
|
|
".bias",
|
|
"_bias",
|
|
".k_scale",
|
|
"_k_scale",
|
|
".v_scale",
|
|
"_v_scale",
|
|
".weight_scale",
|
|
"_weight_scale",
|
|
".input_scale",
|
|
"_input_scale",
|
|
)
|
|
|
|
is_fused_expert = False
|
|
fused_expert_params_mapping = [
|
|
("experts.w13_weight", "experts.gate_up_proj", 0, "w1"),
|
|
("experts.w2_weight", "experts.down_proj", 0, "w2"),
|
|
]
|
|
|
|
num_experts = self.config.num_experts
|
|
|
|
def load_fused_expert_weights(
|
|
name: str,
|
|
params_dict: dict,
|
|
loaded_weight: torch.Tensor,
|
|
shard_id: str,
|
|
num_experts: int,
|
|
):
|
|
param = params_dict[name]
|
|
weight_loader = param.weight_loader
|
|
# let ep moe layer to gracefully handle expert_ids that do not belong to local moe rank
|
|
for expert_id in range(num_experts):
|
|
curr_expert_weight = loaded_weight[expert_id]
|
|
weight_loader(
|
|
param,
|
|
curr_expert_weight,
|
|
name,
|
|
shard_id,
|
|
expert_id,
|
|
)
|
|
return True
|
|
|
|
loaded_params: Set[str] = set()
|
|
params_dict = dict(self.named_parameters(remove_duplicate=False))
|
|
|
|
for name, loaded_weight in weights:
|
|
if "rotary_emb.inv_freq" in name:
|
|
continue
|
|
if "mtp" in name:
|
|
continue
|
|
if "visual" in name:
|
|
continue
|
|
if "language_model" in name:
|
|
name = name.replace(r"model.language_model.", r"model.")
|
|
if ".self_attn." in name:
|
|
name = name.replace(".self_attn", "")
|
|
|
|
for param_name, weight_name, shard_id in stacked_params_mapping:
|
|
if "experts.gate_up_proj" in name or "experts.down_proj" in name:
|
|
is_fused_expert = True
|
|
expert_params_mapping = fused_expert_params_mapping
|
|
|
|
# Skip non-stacked layers and experts (experts handled below).
|
|
if weight_name not in name:
|
|
continue
|
|
|
|
# We have mlp.experts[0].gate_proj in the checkpoint.
|
|
# Since we handle the experts below in expert_params_mapping,
|
|
# we need to skip here BEFORE we update the name, otherwise
|
|
# name will be updated to mlp.experts[0].gate_up_proj, which
|
|
# will then be updated below in expert_params_mapping
|
|
# for mlp.experts[0].gate_gate_up_proj, which breaks load.
|
|
if "mlp.experts" in name:
|
|
continue
|
|
name = name.replace(weight_name, param_name)
|
|
# Skip loading extra parameters for GPTQ/modelopt models.
|
|
if name.endswith(ignore_suffixes) and name not in params_dict:
|
|
continue
|
|
|
|
if name not in params_dict:
|
|
continue
|
|
|
|
param = params_dict[name]
|
|
weight_loader = param.weight_loader
|
|
weight_loader(param, loaded_weight, shard_id)
|
|
break
|
|
else:
|
|
# Track if this is an expert weight to enable early skipping
|
|
is_expert_weight = False
|
|
|
|
for mapping in expert_params_mapping:
|
|
param_name, weight_name, expert_id, shard_id = mapping
|
|
if weight_name not in name:
|
|
continue
|
|
# Anyway, this is an expert weight and should not be
|
|
# attempted to load as other weights later
|
|
is_expert_weight = True
|
|
name_mapped = name.replace(weight_name, param_name)
|
|
if is_fused_expert:
|
|
if "experts.gate_up_proj" in name:
|
|
loaded_weight = loaded_weight.chunk(2, dim=-2)
|
|
load_fused_expert_weights(
|
|
name_mapped,
|
|
params_dict,
|
|
loaded_weight[0],
|
|
"w1",
|
|
num_experts,
|
|
)
|
|
load_fused_expert_weights(
|
|
name_mapped,
|
|
params_dict,
|
|
loaded_weight[1],
|
|
"w3",
|
|
num_experts,
|
|
)
|
|
else:
|
|
load_fused_expert_weights(
|
|
name_mapped,
|
|
params_dict,
|
|
loaded_weight,
|
|
shard_id,
|
|
num_experts,
|
|
)
|
|
else:
|
|
# Skip loading extra parameters for GPTQ/modelopt models.
|
|
if (
|
|
name_mapped.endswith(ignore_suffixes)
|
|
and name_mapped not in params_dict
|
|
):
|
|
continue
|
|
param = params_dict[name_mapped]
|
|
# We should ask the weight loader to return success or
|
|
# not here since otherwise we may skip experts with
|
|
# # other available replicas.
|
|
weight_loader = param.weight_loader
|
|
weight_loader(
|
|
param,
|
|
loaded_weight,
|
|
name_mapped,
|
|
shard_id=shard_id,
|
|
expert_id=expert_id,
|
|
)
|
|
name = name_mapped
|
|
break
|
|
else:
|
|
if is_expert_weight:
|
|
# This is an expert weight but not mapped to this rank, skip all remaining processing
|
|
continue
|
|
|
|
# Skip loading extra parameters for GPTQ/modelopt models.
|
|
if name.endswith(ignore_suffixes) and name not in params_dict:
|
|
continue
|
|
|
|
if name in params_dict.keys():
|
|
param = params_dict[name]
|
|
weight_loader = getattr(
|
|
param, "weight_loader", default_weight_loader
|
|
)
|
|
weight_loader(param, loaded_weight)
|
|
else:
|
|
logger.warning(f"Parameter {name} not found in params_dict")
|
|
loaded_params.add(name)
|
|
|
|
return loaded_params
|
|
|
|
|
|
class Qwen3_5ForConditionalGeneration(Qwen3VLForConditionalGeneration):
|
|
def __init__(
|
|
self,
|
|
config: Qwen3_5Config,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "",
|
|
language_model_cls=Qwen3_5ForCausalLM,
|
|
):
|
|
super().__init__(config, quant_config, prefix, language_model_cls)
|
|
|
|
rope_config = getattr(self.config, "rope_parameters", None) or getattr(
|
|
self.config, "rope_scaling", {}
|
|
)
|
|
self.is_mrope_enabled = "mrope_section" in rope_config
|
|
|
|
self.deepstack_visual_indexes = self.visual.deepstack_visual_indexes
|
|
|
|
@property
|
|
def start_layer(self) -> int:
|
|
return getattr(getattr(self, "model", None), "start_layer", 0)
|
|
|
|
@property
|
|
def end_layer(self) -> int:
|
|
model = getattr(self, "model", None)
|
|
end_layer = getattr(model, "end_layer", None)
|
|
if end_layer is not None:
|
|
return end_layer
|
|
cfg = getattr(model, "config", None)
|
|
return int(getattr(cfg, "num_hidden_layers", 0))
|
|
|
|
def get_embed_and_head(self):
|
|
embed = self.model.embed_tokens.weight if self.pp_group.is_first_rank else None
|
|
head = self.lm_head.weight if self.pp_group.is_last_rank else None
|
|
return embed, head
|
|
|
|
def set_embed_and_head(self, embed, head):
|
|
if self.pp_group.is_first_rank and embed is not None:
|
|
del self.model.embed_tokens.weight
|
|
self.model.embed_tokens.weight = embed
|
|
if self.pp_group.is_last_rank and head is not None:
|
|
del self.lm_head.weight
|
|
self.lm_head.weight = head
|
|
torch.cuda.empty_cache()
|
|
torch.cuda.synchronize()
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
|
stacked_params_mapping = [
|
|
# (param_name, shard_name, shard_id)
|
|
("qkv_proj", "q_proj", "q"),
|
|
("qkv_proj", "k_proj", "k"),
|
|
("qkv_proj", "v_proj", "v"),
|
|
("gate_up_proj", "gate_proj", 0),
|
|
("gate_up_proj", "up_proj", 1),
|
|
]
|
|
|
|
loaded_params: Set[str] = set()
|
|
params_dict = dict(self.named_parameters(remove_duplicate=False))
|
|
for name, loaded_weight in weights:
|
|
if "rotary_emb.inv_freq" in name:
|
|
continue
|
|
if "mtp" in name:
|
|
continue
|
|
if "language_model" in name:
|
|
name = name.replace(r"model.language_model.", r"model.")
|
|
if ".self_attn." in name:
|
|
name = name.replace(".self_attn", "")
|
|
|
|
for param_name, weight_name, shard_id in stacked_params_mapping:
|
|
if weight_name not in name:
|
|
continue
|
|
|
|
if "visual" in name or "mlp.experts" in name:
|
|
continue
|
|
|
|
name = name.replace(weight_name, param_name)
|
|
# Skip loading extra bias for GPTQ models.
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
# Skip layers on other devices.
|
|
# if is_pp_missing_parameter(name, self):
|
|
# continue
|
|
if name not in params_dict:
|
|
continue
|
|
param = params_dict[name]
|
|
weight_loader = getattr(param, "weight_loader")
|
|
weight_loader(param, loaded_weight, shard_id)
|
|
break
|
|
else:
|
|
if "visual" in name:
|
|
# adapt to VisionAttention
|
|
name = name.replace(r"attn.qkv.", r"attn.qkv_proj.")
|
|
name = name.replace(r"model.visual.", r"visual.")
|
|
|
|
# print(name, loaded_weight.shape)
|
|
# Skip loading extra bias for GPTQ models.
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
if name not in params_dict:
|
|
logger.warning(f"Parameter {name} not found in params_dict")
|
|
continue
|
|
param = params_dict[name]
|
|
|
|
weight_loader = getattr(param, "weight_loader", default_weight_loader)
|
|
weight_loader(param, loaded_weight)
|
|
loaded_params.add(name)
|
|
return loaded_params
|
|
|
|
|
|
class Qwen3_5MoeForConditionalGeneration(Qwen3VLForConditionalGeneration):
|
|
"""Qwen3.5 MoE Vision-Language Model."""
|
|
|
|
def __init__(
|
|
self,
|
|
config: Qwen3_5MoeConfig,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "",
|
|
language_model_cls=Qwen3_5MoeForCausalLM,
|
|
) -> None:
|
|
super().__init__(config, quant_config, prefix, language_model_cls)
|
|
rope_config = getattr(self.config, "rope_parameters", None) or getattr(
|
|
self.config, "rope_scaling", {}
|
|
)
|
|
self.is_mrope_enabled = "mrope_section" in rope_config
|
|
|
|
self.deepstack_visual_indexes = self.visual.deepstack_visual_indexes
|
|
|
|
def get_embed_and_head(self):
|
|
embed = self.model.embed_tokens.weight if self.pp_group.is_first_rank else None
|
|
head = self.lm_head.weight if self.pp_group.is_last_rank else None
|
|
return embed, head
|
|
|
|
def set_embed_and_head(self, embed, head):
|
|
if self.pp_group.is_first_rank and embed is not None:
|
|
del self.model.embed_tokens.weight
|
|
self.model.embed_tokens.weight = embed
|
|
if self.pp_group.is_last_rank and head is not None:
|
|
del self.lm_head.weight
|
|
self.lm_head.weight = head
|
|
torch.cuda.empty_cache()
|
|
torch.cuda.synchronize()
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
|
stacked_params_mapping = [
|
|
# (param_name, shard_name, shard_id)
|
|
("qkv_proj", "q_proj", "q"),
|
|
("qkv_proj", "k_proj", "k"),
|
|
("qkv_proj", "v_proj", "v"),
|
|
("gate_up_proj", "gate_proj", 0),
|
|
("gate_up_proj", "up_proj", 1),
|
|
]
|
|
|
|
# Params for weights, fp8 weight scales, fp8 activation scales
|
|
# (param_name, weight_name, expert_id, shard_id)
|
|
expert_params_mapping = FusedMoE.make_expert_params_mapping(
|
|
ckpt_gate_proj_name="gate_proj",
|
|
ckpt_down_proj_name="down_proj",
|
|
ckpt_up_proj_name="up_proj",
|
|
num_experts=self.config.num_experts,
|
|
)
|
|
|
|
# Skip loading extra parameters for GPTQ/modelopt models.
|
|
ignore_suffixes = (
|
|
".bias",
|
|
"_bias",
|
|
".k_scale",
|
|
"_k_scale",
|
|
".v_scale",
|
|
"_v_scale",
|
|
"_weight_scale",
|
|
"_input_scale",
|
|
)
|
|
|
|
is_fused_expert = False
|
|
fused_expert_params_mapping = [
|
|
("experts.w13_weight", "experts.gate_up_proj", 0, "w1"),
|
|
("experts.w2_weight", "experts.down_proj", 0, "w2"),
|
|
]
|
|
|
|
num_experts = self.config.num_experts
|
|
|
|
def load_fused_expert_weights(
|
|
name: str,
|
|
params_dict: dict,
|
|
loaded_weight: torch.Tensor,
|
|
shard_id: str,
|
|
num_experts: int,
|
|
):
|
|
param = params_dict[name]
|
|
weight_loader = param.weight_loader
|
|
# let ep moe layer to gracefully handle expert_ids that do not belong to local moe rank
|
|
for expert_id in range(num_experts):
|
|
curr_expert_weight = loaded_weight[expert_id]
|
|
weight_loader(
|
|
param,
|
|
curr_expert_weight,
|
|
name,
|
|
shard_id,
|
|
expert_id,
|
|
)
|
|
return True
|
|
|
|
loaded_params: Set[str] = set()
|
|
params_dict = dict(self.named_parameters(remove_duplicate=False))
|
|
|
|
for name, loaded_weight in weights:
|
|
if "rotary_emb.inv_freq" in name:
|
|
continue
|
|
if "mtp" in name:
|
|
continue
|
|
if "language_model" in name:
|
|
name = name.replace(r"model.language_model.", r"model.")
|
|
if ".self_attn." in name:
|
|
name = name.replace(".self_attn", "")
|
|
|
|
for param_name, weight_name, shard_id in stacked_params_mapping:
|
|
if name.endswith("experts.gate_up_proj") or name.endswith(
|
|
"experts.down_proj"
|
|
):
|
|
is_fused_expert = True
|
|
expert_params_mapping = fused_expert_params_mapping
|
|
|
|
# Skip non-stacked layers and experts (experts handled below).
|
|
if weight_name not in name:
|
|
continue
|
|
if "visual" in name:
|
|
continue
|
|
|
|
# We have mlp.experts[0].gate_proj in the checkpoint.
|
|
# Since we handle the experts below in expert_params_mapping,
|
|
# we need to skip here BEFORE we update the name, otherwise
|
|
# name will be updated to mlp.experts[0].gate_up_proj, which
|
|
# will then be updated below in expert_params_mapping
|
|
# for mlp.experts[0].gate_gate_up_proj, which breaks load.
|
|
if "mlp.experts" in name:
|
|
continue
|
|
name = name.replace(weight_name, param_name)
|
|
# Skip loading extra parameters for GPTQ/modelopt models.
|
|
if name.endswith(ignore_suffixes) and name not in params_dict:
|
|
continue
|
|
|
|
if name not in params_dict:
|
|
continue
|
|
|
|
param = params_dict[name]
|
|
weight_loader = param.weight_loader
|
|
weight_loader(param, loaded_weight, shard_id)
|
|
break
|
|
else:
|
|
# Track if this is an expert weight to enable early skipping
|
|
is_expert_weight = False
|
|
|
|
for mapping in expert_params_mapping:
|
|
param_name, weight_name, expert_id, shard_id = mapping
|
|
if weight_name not in name:
|
|
continue
|
|
if "visual" in name or self.config.encoder_only:
|
|
continue
|
|
# Anyway, this is an expert weight and should not be
|
|
# attempted to load as other weights later
|
|
is_expert_weight = True
|
|
name_mapped = name.replace(weight_name, param_name)
|
|
if is_fused_expert:
|
|
if "experts.gate_up_proj" in name:
|
|
loaded_weight = loaded_weight.chunk(2, dim=-2)
|
|
load_fused_expert_weights(
|
|
name_mapped,
|
|
params_dict,
|
|
loaded_weight[0],
|
|
"w1",
|
|
num_experts,
|
|
)
|
|
load_fused_expert_weights(
|
|
name_mapped,
|
|
params_dict,
|
|
loaded_weight[1],
|
|
"w3",
|
|
num_experts,
|
|
)
|
|
else:
|
|
load_fused_expert_weights(
|
|
name_mapped,
|
|
params_dict,
|
|
loaded_weight,
|
|
shard_id,
|
|
num_experts,
|
|
)
|
|
else:
|
|
# Skip loading extra parameters for GPTQ models.
|
|
if (
|
|
name_mapped.endswith(ignore_suffixes)
|
|
and name_mapped not in params_dict
|
|
):
|
|
continue
|
|
param = params_dict[name_mapped]
|
|
# We should ask the weight loader to return success or
|
|
# not here since otherwise we may skip experts with
|
|
# # other available replicas.
|
|
weight_loader = param.weight_loader
|
|
weight_loader(
|
|
param,
|
|
loaded_weight,
|
|
name_mapped,
|
|
shard_id=shard_id,
|
|
expert_id=expert_id,
|
|
)
|
|
name = name_mapped
|
|
break
|
|
else:
|
|
if is_expert_weight:
|
|
# This is an expert weight but not mapped to this rank, skip all remaining processing
|
|
continue
|
|
|
|
if "visual" in name:
|
|
# adapt to VisionAttention
|
|
name = name.replace(r"attn.qkv.", r"attn.qkv_proj.")
|
|
name = name.replace(r"model.visual.", r"visual.")
|
|
|
|
# Skip loading extra parameters for GPTQ/modelopt models.
|
|
if name.endswith(ignore_suffixes) and name not in params_dict:
|
|
continue
|
|
|
|
if name in params_dict.keys():
|
|
param = params_dict[name]
|
|
weight_loader = getattr(
|
|
param, "weight_loader", default_weight_loader
|
|
)
|
|
weight_loader(param, loaded_weight)
|
|
else:
|
|
logger.warning(f"Parameter {name} not found in params_dict")
|
|
loaded_params.add(name)
|
|
|
|
self._routed_experts_weights_of_layer = LazyValue(
|
|
lambda: {
|
|
layer_id: layer.mlp.get_moe_weights()
|
|
for layer_id, layer in enumerate(self.model.layers)
|
|
if isinstance(layer.mlp, Qwen2MoeSparseMoeBlock)
|
|
}
|
|
)
|
|
|
|
return loaded_params
|
|
|
|
@property
|
|
def routed_experts_weights_of_layer(self):
|
|
return self._routed_experts_weights_of_layer.value
|
|
|
|
@classmethod
|
|
def get_model_config_for_expert_location(cls, config):
|
|
text_config = getattr(config, "text_config", config)
|
|
return ModelConfigForExpertLocation(
|
|
num_layers=text_config.num_hidden_layers,
|
|
num_logical_experts=text_config.num_experts,
|
|
num_groups=None,
|
|
)
|
|
|
|
|
|
EntryClass = [Qwen3_5MoeForConditionalGeneration, Qwen3_5ForConditionalGeneration]
|